Iterative Semi-Global Matching for Robust Driver Assistance Systems

  • Simon Hermann
  • Reinhard Klette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

Semi-global matching (SGM) is a technique of choice for dense stereo estimation in current industrial driver-assistance systems due to its real-time processing capability and its convincing performance. In this paper we introduce iSGM as a new cost integration concept for semi-global matching. In iSGM, accumulated costs are iteratively evaluated and intermediate disparity results serve as input to generate semi-global distance maps. This novel data structure supports fast analysis of spatial disparity information and allows for reliable search space reduction in consecutive cost accumulation. As a consequence horizontal costs are stabilized which improves the robustness of the matching result. We demonstrate the superiority of this iterative integration concept against a standard configuration of semi-global matching and compare our results to current state-of-the-art methods on the KITTI Vision Benchmark Suite.

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References

  1. 1.
    Ernst, I., Hirschmüller, H.: Mutual Information Based Semi-Global Stereo Matching on the GPU. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 228–239. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Gehrig, S.K., Eberli, F., Meyer, T.: A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 134–143. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI Vision Benchmark Suite. In: Proc. Computer Vision Pattern Recognition, CVPR (2012)Google Scholar
  4. 4.
    Geiger, A., Roser, M., Urtasun, R.: Efficient Large-Scale Stereo Matching. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 25–38. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Gong, M., Yang, Y.-H.: Fast stereo matching using reliability-based dynamic programming and consistency constraints. In: Proc. Int. Conf. Computer Vision (ICCV), vol. 1, pp. 610–617 (2003)Google Scholar
  6. 6.
    Hermann, S., Klette, R.: Evaluation of a New Coarse-to-Fine Strategy for Fast Semi-Global Stereo Matching. In: Ho, Y.-S. (ed.) PSIVT 2011, Part I. LNCS, vol. 7087, pp. 395–406. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Hermann, S., Morales, S., Vaudrey, T., Klette, R.: Illumination Invariant Cost Functions in Semi-Global Matching. In: Koch, R., Huang, F. (eds.) ACCV Workshops 2010, Part II. LNCS, vol. 6469, pp. 245–254. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Hirschmüller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: Proc. IEEE Int. Conf. Computer Vision Pattern Recognition (CVPR), vol. 2, pp. 807–814 (2005)Google Scholar
  9. 9.
    Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. Pattern Analysis Machine Intelligence 31, 1582–1599 (2009)CrossRefGoogle Scholar
  10. 10.
    Meister, S., Jähne, B., Kondermann, D.: Outdoor stereo camera system for the generation of real-world benchmark data sets. Optical Engineering 51, paper 021107, 6 p. (2012)Google Scholar
  11. 11.
    Ohta, Y., Kanade, T.: Stereo by two-level dynamic programming. In: Proc. Int. Joint Conf. Artificial Intelligence (IJCAI), vol. 2, pp. 1120–1126 (1985)Google Scholar
  12. 12.
    Pantillie, C., Nedevschi, S.: SORT-SGM: Subpixel optimized real-time semiglobal matching for intelligent vehicles. IEEE Trans. Vehicular Technology 61, 369–376 (2012)CrossRefGoogle Scholar
  13. 13.
    Ranftl, R., Gehrig, S., Pock, T., Bischof, H.: Pushing the limits of stereo using variational stereo estimation. In: Proc. IEEE Intelligent Vehicles Symposium (IV) (2012) (to appear)Google Scholar
  14. 14.
    Shimizu, M., Okutomi, M.: An analysis of subpixel estimation error on area-based image matching. In: Proc. IEEE Conf. Digital Signal Processing (DSP), vol. 2, pp. 1239–1242 (2002)Google Scholar
  15. 15.
    Warren, H.S.: Hacker’s Delight, pp. 65–72. Addison-Wesley Longman, New York (2002)Google Scholar
  16. 16.
    Yamaguchi, K., Hazan, T., McAllester, D., Urtasun, R.: Continuous Markov random fields for robust stereo estimation, arXiv:1204.1393v1 (2012)Google Scholar
  17. 17.
    Zach, C., Pock, T., Bischof, H.: A Duality Based Approach for Realtime TV-L 1 Optical Flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Zabih, R., Woodfill, J.: Non-Parametric Local Transforms for Computing Visual Correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151–158. Springer, Heidelberg (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Simon Hermann
    • 1
  • Reinhard Klette
    • 1
  1. 1.The .enpeda.. Project, Department of Computer ScienceThe University of AucklandNew Zealand

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